The approximate determination of the integral distribution function of the largest values in the realizations of random sequences

1972 ◽  
Vol 15 (1) ◽  
pp. 45-47
Author(s):  
Ya. A. Fomin
Keyword(s):  
Distribution Function ◽  
Approximate Determination ◽  
Random Sequences ◽  
Integral Distribution ◽  
Integral Distribution Function

scholarly journals STRESS INDEX IN UKRAINE’S MARKET OF NEGOTIABLE FINANCIAL INSTRUMENTS

2018 ◽  
Vol 2018 (2) ◽  
pp. 39-49
Author(s):  
Igor KRAVCHUK ◽  
Keyword(s):  
Distribution Function ◽  
Composite Index ◽  
Stress Index ◽  
Seasonal Adjustment ◽  
Efficiency Coefficient ◽  
Financial Instruments ◽  
Factors Affecting ◽  
State Of The Economy ◽  
Integral Distribution ◽  
Integral Distribution Function

Market of negotiable financial instruments is an immanent component of the financial system and is in a two-way relationship with other financial institutions and real sector of the economy in terms of ensuring its stable functioning. Possible market shocks can adversely affect state of the economy; therefore regulators should carry out constant market surveillance to detect and prevent early possible market violations, by calculating (in particular) the composite stress index. To construct a composite index, correlation analysis, generalized autoregressive conditional heteroscedasticity model, standardization based on the integral distribution function, seasonal adjustment and determination of a long-term trend based on filtering are used. It is proposed to calculate the stress index of Ukraine’s market of negotiable financial instruments on the basis of market data by balanced averaging of the following sub-indices: (i) stocks (UX stock yield volatility, CMAX indicator, market efficiency coefficient); (ii) debt securities (sovereign spread and CDS spread); and (iii) derivatives (indicator of the change in the number of open futures positions for the UX stock). Aforementioned were standardized using the integral distribution function. The author’s analysis of the proposed composite stress index shows that dominant factors affecting the situation in Ukraine’s market of securities and derivatives are intra-national ones, which have become dominant since 2014. At present, the stress index of Ukraine’s market of negotiable financial instruments is still of little importance to reflect economic situation in the state, given weak development of the market and its meager role for financing and reflecting the corporate activity.

КОКУС ЧОҢДУКТАРДЫН ЫКТЫМАЛДУУЛУКТАРЫН БӨЛҮШТҮРҮҮ ФУНКЦИЯСЫН ОКУТУУНУН ЫКМАЛАРЫ

2020 ◽  
pp. 168-173
Author(s):  
Аалиева Бурул
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Distribution Density ◽  
Random Variables ◽  
Random Variable ◽  
Continuous Random Variable ◽  
Integral Distribution ◽  
Integral Distribution Function ◽  
Range Of Values

Аннотация: Бөлүштүрүү функциясын, үзгүлтүксүз кокус чоңдуктардын ыктымалдуулуктарын бѳлүштүрүүнүн жиктелиш функциясы (ыктымалдуулуктун тыгыздыгы), ыктымалдуулуктарды бир калыпта бѳлуштүрүү законун аныктоо. Бөлүштүрүү функциясынын касиеттерин окутуу, далилдөө. X кокус чоңдугунун кабыл алууга мүмкүн болгон маанилери (a,b) интервалында жаткандыгынын ыктымалдуулугу бөлүштүрүү функциясынын өсүндүсүнө барабар. Түйүндүү сѳздѳр: Бөлүштурүү функциясы, үзгүлтүксүз кокус чоңдуктардын ыктымалдуулуктары, дискреттик кокус чоңдук, бөлүштүрүүнүн интегралдык функциясы, баштапкы функция. Аннотация: Определять вид непрерывной случайной величины, находить вероятность попадания случайной величины в заданный интервал по заданной функции распределения, уметь находить плотность распределения и равномерное распределения. Еще одно отличие характеристики случайных величин непрерывного действия-включение функции классификации распределения вероятностей, обнаружение первого производного функции последовательности. Следовательно, характеристика распределения вероятностей дискретных случайных величин. Свойства функции распределения обучения и доказательства. Х может быть, чтобы принять параметры диапазона значений (а, б), что функция распределения вероятностей равна приращению. Ключевые слова: Функция распределения, вероятность непрерывной случайной величины, дискретная случайная величина, интегральная функция распределения, первообразная. Annotation: Determine the type of random variable, find the probability of a random variable falling into a given interval by a given distribution function, be able to find the distribution density and uniform distribution. Properties of learning distribution function and evidence. X maybe to take the parameters of the range of values (a, b), that the probability distribution function is equal to the increment. Another difference in the characterization of continuous random variables is the inclusion of the classification function of the probability distribution, the detection of the first derivative of the sequence function. Hence, the characteristic of the probability distribution of discrete random variables Non-decreasing functions, ∫ _ (- ∞) ^ ∞▒ 〖P (x) ax = 1〗. In the case of an individual, if the values of a random variable (a, b) are located within ∫_a ^ b▒ 〖P (x) ax = 1〗 Keywords: Distribution function, probability of continuous random variable, discrete random variable, integral distribution function, antiderivative. DOI: 10.35254/bhu.2019.50.1 ВЕСТНИК БИШКЕКСКОГО ГОСУДАРСТВЕННОГО УНИВЕРСИТЕТА. No4(50) 2019 169 Аннотация: Бөлүштүрүү функциясын, үзгүлтүксүз кокус чоңдуктардын ыктымалдуулуктарын бѳлүштүрүүнүн жиктелиш функциясы (ыктымалдуулуктун тыгыздыгы), ыктымалдуулуктарды бир калыпта бѳлуштүрүү законун аныктоо. Бөлүштүрүү функциясынын касиеттерин окутуу, далилдөө. X кокус чоңдугунун кабыл алууга мүмкүн болгон маанилери (a,b) интервалында жаткандыгынын ыктымалдуулугу бөлүштүрүү функциясынын өсүндүсүнө барабар. X кокус чондугу PP(xx < xx1) ыктымалдуулукта x ден кичине маанилерди кабыл алат; X кокус чондугу xx1 ≤ xx < xx2барабарсыздыктын ыктымалдуулугу PP(xx1 ≤ xx < xx2) түрүндө канааттандырат. Үзгүлтүксүз кокус чоңдуктарды мүнөздөөнүн дагы бир башкача жолу ыктымалдуулукту бөлүштүрүүнүн жиктелиш функциясын киргизүү, тутамдык функциясынын биринчи туундусун табуу. Демек,тутамдык функция жиктелиш функциясынын баштапкы функциясы болорун, дискреттик кокус чондуктардын ыктымалдуулуктарынын бөлүштүрүүсүн мунөздөө. Жиктелиш функциясы кемибөөчү функция, ∫ ff(xx)dddd = 1 ∞ −∞ . Жекече учурда, эгерде кокус чоңдуктардын мүмкүн болгон маанилери (a,b) аралыгында жайгашса, анда � ff(xx)dddd = 1 bb aa Түйүндүү сѳздѳр: Бөлүштурүү функциясы, үзгүлтүксүз кокус чоңдуктардын ыктымалдуулуктары, дискреттик кокус чоңдук, бөлүштүрүүнүн интегралдык функциясы, баштапкы функция. Аннотация: Определять вид непрерывной случайной величины, находить вероятность попадания случайной величины в заданный интервал по заданной функции распределения, уметь находить плотность распределения и равномерное распределения. Еще одно отличие характеристики случайных величин непрерывного действия-включение функции классификации распределения вероятностей, обнаружение первого производного функции последовательности. Следовательно, характеристика распределения вероятностей дискретных случайных величин. Ключевые слова: Функция распределения, вероятность непрерывной случайной величины, дискретная случайная величина, интегральная функция распределения, первообразная. Annotation: Determine the type of random variable, find the probability of a random variable falling into a given interval by a given distribution function, be able to find the distribution density and uniform distribution. Properties of learning distribution function and evidence. X maybe to take the parameters of the range of values (a, b), that the probability distribution function is equal to the increment. Another difference in the characterization of continuous random variables is the inclusion of the classification function of the probability distribution, the detection of the first derivative of the sequence function. Keywords: Distribution function, probability of continuous random variable, discrete random variable, integral distribution function, antiderivative.

scholarly journals F(E,Lz) for Kent's Model of the Galactic Bulge?

1996 ◽  
Vol 169 ◽  
pp. 351-352
Author(s):  
Walter Dehnen
Keyword(s):  
Distribution Function ◽  
Galactic Bulge ◽  
Physical Reason ◽  
Integral Distribution ◽  
Integral Distribution Function

Using the Richardson-Lucy algorithm the two-integral distribution function (2I-DF) f(E,Lz) for Kent's (1992) Bulge model has been constructed. It turns out to be negative (and hence unphysical) at Lz ≈ Lc. The physical reason for this result and its implications are discussed.

Influence of extinction phenomenon on determination of the orientation distribution function

2006 ◽  
Vol 2006 (suppl_23_2006) ◽  
pp. 175-180
Author(s):  
G. Gómez-Gasga ◽  
T. Kryshtab ◽  
J. Palacios-Gómez ◽  
A. de Ita de la Torre
Keyword(s):  
Distribution Function ◽  
Orientation Distribution Function ◽  
Orientation Distribution

DETERMINATION OF THE DISTRIBUTION FUNCTION OF THE TIME BETWEEN FAILURES OF A WEAPON MODEL WITHOUT TAKING INTO ACCOUNT THE ENEMY’S FIRE IMPACT

2019 ◽  
pp. 39-45
Author(s):  
M. Sliusarenko ◽  
O. Semenenko ◽  
T. Akinina ◽  
O. Zaritsky ◽  
V. Ivanov
Keyword(s):  
Distribution Function ◽  
Analytical Description ◽  
Time To Failure ◽  
Missile Defense ◽  
Incorrect Choice ◽  
Military Equipment ◽  
Time Between Failures ◽  
The Military ◽  
Fire Impact

In the article, based on the analysis of the requirements for the readiness of weapons and military equipment during combat use and the reliability of their operation in the course of combat operations, it was discovered that one of the reasons that causes a discrepancy between the declared failures and real ones may be the incorrect choice and justification of the time distribution function up to the refusal of military means. As a rule, during the development of these tools, the function of distribution of time to failure is chosen by analogy with similar patterns of weapons and military equipment. In the theory of reliability, special attention is given to choosing the function of time-breaking non-response (failures or failures). Therefore, the article deals with the questions of evaluating the effectiveness of functioning of complex systems and methods of modeling the processes of their functioning, taking into account the laws of the distribution of random variables. The discrepancy between the declared irregularity of the military apparatus and the fact that is actually observed in the troops can be explained by the incorrectly accepted hypothesis about the distribution of time to failure. Therefore, the article analyzes the order of the justification of such a function without taking into account the enemy's fire impact and the proposed variant of determining the function of distribution of the time of work until the refusal of the model of military equipment. The article also cites the reasons for the discrepancy between the claimed missile defense equipment and what is actually observed in the troops. The proposed mathematical model of faultlessness, which at stages of designing and design will allow to set requirements to the model of technology with the help of analytical description. The sequence of calculations of non-failure indexes based on the use of Weibull distribution is substantiated.

Determination of thermodynamic state variables of liquids from its microscopic structure using an artificial neural network

Soft Matter ◽  
2020 ◽  
Author(s):  
Ulices Que-Salinas ◽  
Pedro Ezequiel Ramirez-Gonzalez ◽  
Alexis Torres-Carbajal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Distribution Function ◽  
Microscopic Structure ◽  
Machine Learning Method ◽  
Learning Method ◽  
State Variables ◽  
Thermodynamic State

In this work we implement a machine learning method to predict the thermodynamic state of a liquid using only its microscopic structure provided by the radial distribution function (RDF). The...

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